import torch
from torch import nn
from torch.utils.data import DataLoader
from torchvision import datasets
from torchvision.transforms import ToTensor


class NeuralNetworks(nn.Module):

    def __init__(self):
        super().__init__()
        self.flatten = nn.Flatten()
        self.linear_relu_stack = nn.Sequential(
            nn.Linear(28 * 28, 512),
            nn.ReLU(),
            nn.Linear(512, 512),
            nn.ReLU(),
            nn.Linear(512, 10)
        )

    def forward(self, x):
        x = self.flatten(x)
        logits = self.linear_relu_stack(x)
        return logits


def train(device, dataloader, model, loss_fn, optimizer):
    size = len(dataloader.dataset)
    model.train()
    for batch, (X, y) in enumerate(dataloader):
        X, y = X.to(device), y.to(device)

        pred = model(X)
        loss = loss_fn(pred, y)

        loss.backward()
        optimizer.step()
        optimizer.zero_grad()

        if batch % 100 == 0:
            loss, current = loss.item(), (batch + 1) * len(X)
            print(f'loss: {loss:>7f} [{current:>5d}/{size:>5d}]')

    return model


def test(device, dataloader, model, loss_fn):
    size = len(dataloader.dataset)
    num_batches = len(dataloader)
    model.eval()
    test_loss, correct = 0, 0
    with torch.no_grad():
        for X, y in dataloader:
            X, y = X.to(device), y.to(device)
            pred = model(X)
            test_loss += loss_fn(pred, y).item()
            correct += (pred.argmax(1) == y).type(torch.float).sum().item()
    test_loss /= num_batches
    correct /= size
    print(f'The Error: \n Accuracy: {(100 * correct):>0.1f}%, Avg loss: {test_loss:>8f} \n')

    return correct, test_loss


def main():
    training_data = datasets.FashionMNIST(
        root='./data',
        train=True,
        download=False,
        transform=ToTensor(),
    )

    test_data = datasets.FashionMNIST(
        root='./data',
        train=False,
        download=False,
        transform=ToTensor(),
    )

    batch_size = 64

    train_dataloader = DataLoader(training_data, batch_size=batch_size)
    test_dataloader = DataLoader(test_data, batch_size=batch_size)

    for X, y in test_dataloader:
        print(f'Shape of X [N, C, H, W]： {X.shape}')
        print(f'Shape of y: {y.shape} {y.dtype}')
        break

    device = (
        'cuda'
        if torch.cuda.is_available()
        else 'mps'
        if torch.backends.mps.is_available()
        else 'cpu'
    )
    print(f'Using {device} device')

    model = NeuralNetworks().to(device)
    print(model)

    loss_fn = nn.CrossEntropyLoss()
    optimizer = torch.optim.SGD(model.parameters(), lr=1e-3)

    epochs = 20
    for t in range(epochs):
        print(f'Epoch {t+1}\n-------------------------------------------')
        train(
            device=device,
            dataloader=train_dataloader,
            model=model,
            loss_fn=loss_fn,
            optimizer=optimizer
        )
        test(device, test_dataloader, model, loss_fn)
    print('Done!')

    torch.save(model.state_dict(), 'model.pth')
    # model = NeuralNetwork().to(device)
    # model.load_state_dict(torch.load('model.pth'))
    
    return


if __name__ == '__main__':
    main()
